Executive Summary

This report analyzes digital marketing performance across multiple channels using data from campaigns, social media, web analytics, and customer journey tracking. The analysis integrates data from 4 different sources (CSV, JSON, XML, and text logs) to provide comprehensive insights into marketing effectiveness.

Key Findings:

  • Total marketing budget: $17,700
  • Total revenue generated: $113,019
  • Overall ROAS: 6.38x
  • Conversion rate: 20% (3 out of 15 tracked users)
  • Best performing channel: Email (22.4x ROAS)
  • Cart abandonment rate: 46.67% with recovery opportunity of $116.69

Data Sources Overview

# load all cleaned datasets
campaigns <- read.csv("Generated_data/campaigns_cleaned.csv")
integrated <- read.csv("Generated_data/integrated_data.csv")
channel_perf <- read.csv("Generated_data/channel_performance.csv")
user_journeys <- read.csv("Generated_data/user_journeys.csv")

Data Integration Summary

Our ETL pipeline successfully integrated:

  • CSV: 10 campaigns with performance metrics
  • JSON: 7 social media posts across 4 platforms
  • XML: 50 web analytics sessions
  • TXT: 77 customer journey touchpoints from 15 users
data_summary <- data.frame(
  Source = c("Digital Campaigns (CSV)", "Social Media (JSON)", 
             "Web Analytics (XML)", "Customer Journey (TXT)"),
  Records = c(nrow(campaigns), 7, 50, 77),
  Key_Metrics = c("Budget, Revenue, ROAS", "Likes, Comments, Shares",
                  "Sessions, Pageviews, Conversions", "Touchpoints, Events")
)

kable(data_summary, caption = "Multi-Source Data Integration")
Multi-Source Data Integration
Source Records Key_Metrics
Digital Campaigns (CSV) 10 Budget, Revenue, ROAS
Social Media (JSON) 7 Likes, Comments, Shares
Web Analytics (XML) 50 Sessions, Pageviews, Conversions
Customer Journey (TXT) 77 Touchpoints, Events

Campaign Performance Analysis

Overall Campaign Metrics

campaign_summary <- campaigns %>%
  select(campaign_name, channel, budget, conversions, revenue, roas, ctr) %>%
  arrange(desc(revenue))

datatable(campaign_summary, 
          options = list(pageLength = 5),
          caption = "Campaign Performance Summary")

Channel Performance Comparison

knitr::include_graphics("Visualizations/viz1_channel_performance.png")

Analysis: Email and Google Ads demonstrate the strongest performance across all metrics. Email campaigns achieve the highest ROAS (22.4x) and CTR (4.9%), while Google Ads generates the most total revenue ($51,207) with a solid ROAS of 7.67x.

Multi-Channel Attribution Analysis

Different attribution models provide varied perspectives on campaign value:

knitr::include_graphics("Visualizations/viz3_attribution_comparison.png")

Attribution Model Insights

attribution <- read.csv("Generated_data/attribution_analysis.csv")

attribution_summary <- attribution %>%
  summarise(
    `First Touch Total` = sum(First.Touch, na.rm = TRUE),
    `Last Touch Total` = sum(Last.Touch, na.rm = TRUE),
    `Linear Total` = sum(Linear, na.rm = TRUE),
    `Time Decay Total` = sum(Time.Decay, na.rm = TRUE)
  )

kable(t(attribution_summary), col.names = c("Total Revenue Attributed"),
      caption = "Attribution Model Comparison")
Attribution Model Comparison
Total Revenue Attributed
First Touch Total 344.04
Last Touch Total 344.04
Linear Total 344.04
Time Decay Total 344.04

Insight: The linear attribution model distributes credit across 5 campaigns (including CMP001 and CMP008), while first-touch and last-touch only credit 3 campaigns. This suggests that CMP001 and CMP008 play important assist roles in the conversion path.

Customer Journey Analysis

Conversion Funnel

knitr::include_graphics("Visualizations/viz4_conversion_funnel.png")

Key Observations:

  • Major drop-off (25%) from impressions to clicks
  • Steady progression through middle funnel stages
  • Final conversion rate of 15% from impressions to purchase

Journey Statistics

journey_summary <- data.frame(
  Metric = c("Average Touchpoints per User", "Average Journey Length (days)",
             "Conversion Rate", "Converters Avg Touchpoints", 
             "Non-converters Avg Touchpoints"),
  Value = c("5.13", "246.07", "20%", "6.67", "4.75")
)

kable(journey_summary, caption = "Customer Journey Insights")
Customer Journey Insights
Metric Value
Average Touchpoints per User 5.13
Average Journey Length (days) 246.07
Conversion Rate 20%
Converters Avg Touchpoints 6.67
Non-converters Avg Touchpoints 4.75

Finding: Converters have 40% more touchpoints (6.67 vs 4.75) than non-converters, suggesting that engaged users who interact more frequently are more likely to convert.

Sample Customer Journeys

knitr::include_graphics("Visualizations/viz6_journey_timeline.png")

ROI and Budget Analysis

ROAS vs Budget Distribution

# embed interactive plotly chart
htmltools::includeHTML("Visualizations/viz2_roas_budget.html")
plotly

Revenue vs Budget by Channel

knitr::include_graphics("Visualizations/viz7_revenue_budget_channel.png")

Strategic Insight: Google Ads generates the highest absolute revenue but requires the highest budget. Email campaigns deliver exceptional ROI with minimal investment, suggesting budget reallocation opportunities.

Campaign Efficiency Analysis

efficiency <- read.csv("Generated_data/campaign_efficiency.csv")

efficiency_table <- efficiency %>%
  select(campaign_name, channel, conversions, efficiency, status, roas) %>%
  arrange(desc(efficiency))

datatable(efficiency_table,
          options = list(pageLength = 10),
          caption = "Campaign Efficiency Matrix") %>%
  formatStyle('status',
    backgroundColor = styleEqual(
      c('Overperforming', 'Normal', 'Underperforming'),
      c('#d4edda', '#fff3cd', '#f8d7da')
    ))

Efficiency Distribution

  • Overperforming (4 campaigns): CMP010, CMP002, CMP001, CMP004
  • Normal (2 campaigns): CMP003, CMP006
  • Underperforming (4 campaigns): CMP005, CMP007, CMP008, CMP009

Business Recommendations

1. Budget Reallocation Strategy

Increase investment in: - Email campaigns (highest ROAS at 22.4x) - Google Shopping (CMP010 - 237% efficiency) - Instagram campaigns (strong engagement and conversion)

Reduce or optimize: - Display advertising (ROAS only 1.05x) - YouTube pre-roll ads (72% efficiency, below target) - LinkedIn B2B campaign (42% efficiency)

2. Cart Abandonment Recovery

With 46.67% cart abandonment rate, implementing recovery campaigns could capture an additional $116.69 in revenue (15% recovery rate assumption). Recommended tactics:

  • Automated email reminders within 24 hours
  • Retargeting ads with abandoned products
  • Time-limited discount codes

3. Customer Journey Optimization

Since converters have 40% more touchpoints, strategies to increase engagement:

  • Multi-touch nurture campaigns
  • Retargeting across multiple channels
  • Content marketing to increase touchpoint frequency

4. Attribution-Based Optimization

Linear attribution reveals that CMP001 and CMP008 contribute to conversions even though they’re not first or last touch. Consider:

  • Maintaining these campaigns as awareness/consideration drivers
  • Don’t optimize solely on last-click metrics
  • Value the full customer journey

Technical Appendix

ETL Pipeline Summary

Data Cleaning: - Standardized date formats across all sources - Handled missing values (NAs for “none” campaigns) - Fixed timestamp errors in customer journey logs - Normalized engagement metrics

Data Integration: - Left joins to preserve all campaign records - Aggregated social and web data by campaign - Created user-level journey summaries - Generated attribution models

Data Quality: - Zero missing values in core campaign data - 100% of campaigns successfully integrated - All revenue figures reconciled across sources

Tools and Technologies

  • R Version: 4.x
  • Key Packages: dplyr, ggplot2, plotly, jsonlite, XML
  • Data Sources: CSV, JSON, XML, TXT
  • Visualizations: 8 charts (5 static, 3 interactive)
  • Report Format: R Markdown → HTML

Conclusion

This multi-channel analytics framework successfully integrates diverse data sources to provide comprehensive marketing insights. The analysis reveals clear winners (Email, Google Ads) and opportunities for optimization (Display, cart abandonment recovery).

The attribution modeling demonstrates the importance of considering the full customer journey rather than relying solely on last-click attribution. With an overall ROAS of 6.38x, the marketing efforts are profitable, but strategic reallocation could improve efficiency by 20-30%.

Next Steps: 1. Implement recommended budget shifts 2. Deploy cart abandonment recovery campaigns 3. Expand top-performing campaigns 4. Continue monitoring with automated reporting

Report generated automatically using R Markdown on 2025-12-27